Remotely sensed and modeled soil moisture products are widely used in hydro-meteorological, agricultural and other applications. However, both of them need to be validated by ground measurements before they can be used for different applications. Three remotely sensed soil moisture products and the simulated soil moisture using the Community Land Model version 4.0 (CLM4.0) were compared and verified with the in situ measurements from a mesoscale soil moisture monitoring network in the central Tibetan Plateau area. These four soil moisture data sets can overall reasonably capture the surface soil moisture dynamics but with considerable biases for some certain soil conditions, such as frozen. The Land Parameter Retrieval Model (LPRM) AMSR-E soil moisture systematically overestimates unfrozen soil moisture and fails to estimates frozen soil, with bias (BIAS) and root mean square error (RMSE) values of 0.11m³ m⁻³ and 0.12m³ m⁻³ in average, respectively. The Japan Aerospace Exploration Agency (JAXA) AMSR-E soil moisture shows better performance than the other two remotely sensed soil moisture, with smaller BIAS and RMSE values (0.017 and 0.096m³ m⁻³ in average, respectively). But it still has a large overestimation bias for unfrozen soil. The AMSR2 data show a similar performance to the JAXA AMSR-E product, but with slight larger BIAS and RMSE (0.024 and 0.112m³ m⁻³ in average, respectively), and a very large short-term variability during unfrozen periods. The CLM4.0 model was overall able to model multiple-layers' soil moistures well (RMSE =0.03m³ m⁻³) but slightly underestimated surface soil moisture (0–10cm) and overestimated deeper soil moisture (20–40cm) under unfrozen conditions. These comparisons suggest that the LPRM algorithm fails to retrieve soil moisture value in the Tibetan Plateau area (TP) and the JAXA algorithm needs to be improved for unfrozen soil. For the AMSR2 product, more calibration work and to align it to the AMSR-E sensor are needed. This study also show the potential of using the land surface models (LSMs) for verify the remotely sensed soil moisture products especially for the area with lack of observations available, such as in the Tibetan Plateau area.

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italicbiasitalic 011hsp sp025msup locpost3sup hsp sp025msup locposthsp sp0103sup lprm algorithm overestimation bias community land model version 40 clm40 italicrmseitalic hydrometeorological agricultural and other applications sensed and modeled soil moisture products root mean square error conditions italicin situitalic measurements land surface models lsms shortterm variability multiplelayers soil moistures land parameter retrieval model lprm amsre soil moisture systematically overestimates unfrozen soil moisture amsr2

Jing Chen, Huifang Zhang,Shaobo Sun, Baozhang Chen, Mingliang Che,.Comparison of remotely-sensed and modeled soil moisture using CLM4.0 with in situ measurements in the central Tibetan Plateau area. 129 (0),31-44.

Jing Chen, et al."Comparison of remotely-sensed and modeled soil moisture using CLM4.0 with in situ measurements in the central Tibetan Plateau area" 129

Jing Chen, Huifang Zhang,Shaobo Sun, Baozhang Chen, Mingliang Che,"Comparison of remotely-sensed and modeled soil moisture using CLM4.0 with in situ measurements in the central Tibetan Plateau area"